A new idea for RSA backdoors

Galois上的多素数RSA方法

Multi-Prime RSA Over Galois Approach

RSA密码系统的许多变体存在于文献中。其中之一是基于Galois方法的多项式RSA。在标准的RSA模是两个大素数的乘积，而在伽罗瓦方法中，作者认为模是两个可积多项式的乘积。我们使用这个想法，并扩展多素数RSA多项式。

两种基于RSA的密码体制

Two RSA-based Cryptosystems

本研究针对密码学领域的密码体制设计问题，将环中本原m次单位根思想与离散傅里叶变换融合，提出RSA-DFT和RSA-HGR两种新型RSA基密码体制，丰富了相关密码方案。

A Variant RSA Acceleration with Parallelization

The standard RSA relies on multiple big-number modular exponentiation operations and longer key-length is required for better protection. This imposes a hefty time penalty for encryption and decryption. In this study, we analyzed and developed an improved parallel algorithm (PMKRSA) based on the idea of splitting the plaintext into multiple chunks and encrypt the chunks using multiple key-pairs. The algorithm in our new scheme is so natural for parallelized implementation that we also investigated its parallelization in a GPU environment. In the following, the structure of our new scheme is ou…

通过受限的瑞尼熵优化提升RSA的量子抗性：一种用于向后兼容加密的理论框架

Towards Enhanced Quantum Resistance for RSA via Constrained Rényi Entropy Optimization: A Theoretical Framework for Backward-Compatible Cryptography

本文提出CREO框架，通过限制RSA素数接近度以优化瑞尼熵，提升RSA的量子抗性，同时保持向后兼容性。

One-shot Neural Backdoor Erasing via Adversarial Weight Masking

Recent studies show that despite achieving high accuracy on a number of real-world applications, deep neural networks (DNNs) can be backdoored: by injecting triggered data samples into the training dataset, the adversary can mislead the trained model into classifying any test data to the target class as long as the trigger pattern is presented. To nullify such backdoor threats, various methods have been proposed. Particularly, a line of research aims to purify the potentially compromised model. However, one major limitation of this line of work is the requirement to access sufficient original …

Can Adversarial Weight Perturbations Inject Neural Backdoors?

Adversarial machine learning has exposed several security hazards of neural models and has become an important research topic in recent times. Thus far, the concept of an "adversarial perturbation" has exclusively been used with reference to the input space referring to a small, imperceptible change which can cause a ML model to err. In this work we extend the idea of "adversarial perturbations" to the space of model weights, specifically to inject backdoors in trained DNNs, which exposes a security risk of using publicly available trained models. Here, injecting a backdoor refers to obtaining…